Bibliografie

(eng): Various estimators for the standard nonlinear regression model are compared with a focus on methods which are robust to outlying measurements in the data. The main contribution is a metalearning study which has the aim to predict the most suitable estimator for a particular data set. Here, various versions of the nonlinear least weighted squares estimator are compared with nonlinear least squares, nonlinear least trimmed squares and a nonlinear regression median, where the last is a special case of nonlinear regression quantiles. The metalearning study is performed over a data base of 24 economic data sets. The nonlinear least weighted squares estimator is able to yield the best result for the most data sets. The metalearning study gives advice how to select appropriate weights for the nonlinear least weighted squares, particularly it reveals tests of normality and heteroscedasticity to play a crucial role in finding suitable weights.